Overview

Dataset statistics

Number of variables20
Number of observations149
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.4 KiB
Average record size in memory160.9 B

Variable types

Categorical3
Numeric17

Warnings

Ladder score in Dystopia has constant value "2.43" Constant
Country name has a high cardinality: 149 distinct values High cardinality
Ladder score is highly correlated with upperwhisker and 9 other fieldsHigh correlation
Standard error of ladder score is highly correlated with lowerwhisker and 6 other fieldsHigh correlation
upperwhisker is highly correlated with Ladder score and 10 other fieldsHigh correlation
lowerwhisker is highly correlated with Ladder score and 10 other fieldsHigh correlation
Logged GDP per capita is highly correlated with Ladder score and 8 other fieldsHigh correlation
Social support is highly correlated with Ladder score and 8 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Ladder score and 8 other fieldsHigh correlation
Freedom to make life choices is highly correlated with Ladder score and 3 other fieldsHigh correlation
Generosity is highly correlated with Explained by: GenerosityHigh correlation
Perceptions of corruption is highly correlated with Explained by: Perceptions of corruptionHigh correlation
Explained by: Log GDP per capita is highly correlated with Ladder score and 8 other fieldsHigh correlation
Explained by: Social support is highly correlated with Ladder score and 8 other fieldsHigh correlation
Explained by: Healthy life expectancy is highly correlated with Ladder score and 8 other fieldsHigh correlation
Explained by: Freedom to make life choices is highly correlated with Ladder score and 3 other fieldsHigh correlation
Explained by: Generosity is highly correlated with GenerosityHigh correlation
Explained by: Perceptions of corruption is highly correlated with Perceptions of corruptionHigh correlation
Dystopia + residual is highly correlated with upperwhiskerHigh correlation
Ladder score is highly correlated with Standard error of ladder score and 10 other fieldsHigh correlation
Standard error of ladder score is highly correlated with Ladder score and 8 other fieldsHigh correlation
upperwhisker is highly correlated with Ladder score and 10 other fieldsHigh correlation
lowerwhisker is highly correlated with Ladder score and 10 other fieldsHigh correlation
Logged GDP per capita is highly correlated with Ladder score and 8 other fieldsHigh correlation
Social support is highly correlated with Ladder score and 8 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Ladder score and 8 other fieldsHigh correlation
Freedom to make life choices is highly correlated with Ladder score and 3 other fieldsHigh correlation
Generosity is highly correlated with Explained by: GenerosityHigh correlation
Perceptions of corruption is highly correlated with Explained by: Perceptions of corruptionHigh correlation
Explained by: Log GDP per capita is highly correlated with Ladder score and 8 other fieldsHigh correlation
Explained by: Social support is highly correlated with Ladder score and 8 other fieldsHigh correlation
Explained by: Healthy life expectancy is highly correlated with Ladder score and 8 other fieldsHigh correlation
Explained by: Freedom to make life choices is highly correlated with Ladder score and 3 other fieldsHigh correlation
Explained by: Generosity is highly correlated with GenerosityHigh correlation
Explained by: Perceptions of corruption is highly correlated with Perceptions of corruptionHigh correlation
Ladder score is highly correlated with upperwhisker and 8 other fieldsHigh correlation
Standard error of ladder score is highly correlated with Logged GDP per capita and 2 other fieldsHigh correlation
upperwhisker is highly correlated with Ladder score and 8 other fieldsHigh correlation
lowerwhisker is highly correlated with Ladder score and 8 other fieldsHigh correlation
Logged GDP per capita is highly correlated with Ladder score and 9 other fieldsHigh correlation
Social support is highly correlated with Ladder score and 8 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Ladder score and 8 other fieldsHigh correlation
Freedom to make life choices is highly correlated with Ladder score in Dystopia and 1 other fieldsHigh correlation
Generosity is highly correlated with Ladder score in Dystopia and 1 other fieldsHigh correlation
Perceptions of corruption is highly correlated with Ladder score in Dystopia and 1 other fieldsHigh correlation
Ladder score in Dystopia is highly correlated with Ladder score and 9 other fieldsHigh correlation
Explained by: Log GDP per capita is highly correlated with Ladder score and 8 other fieldsHigh correlation
Explained by: Social support is highly correlated with Ladder score and 7 other fieldsHigh correlation
Explained by: Healthy life expectancy is highly correlated with Ladder score and 7 other fieldsHigh correlation
Explained by: Freedom to make life choices is highly correlated with Freedom to make life choicesHigh correlation
Explained by: Generosity is highly correlated with GenerosityHigh correlation
Explained by: Perceptions of corruption is highly correlated with Perceptions of corruptionHigh correlation
Explained by: Healthy life expectancy is highly correlated with Ladder score and 13 other fieldsHigh correlation
Ladder score is highly correlated with Explained by: Healthy life expectancy and 13 other fieldsHigh correlation
Regional indicator is highly correlated with Explained by: Healthy life expectancy and 14 other fieldsHigh correlation
Dystopia + residual is highly correlated with Ladder score and 4 other fieldsHigh correlation
Explained by: Freedom to make life choices is highly correlated with Explained by: Healthy life expectancy and 10 other fieldsHigh correlation
upperwhisker is highly correlated with Explained by: Healthy life expectancy and 13 other fieldsHigh correlation
Social support is highly correlated with Explained by: Healthy life expectancy and 12 other fieldsHigh correlation
Generosity is highly correlated with Explained by: Healthy life expectancy and 3 other fieldsHigh correlation
Freedom to make life choices is highly correlated with Explained by: Healthy life expectancy and 10 other fieldsHigh correlation
Explained by: Log GDP per capita is highly correlated with Explained by: Healthy life expectancy and 13 other fieldsHigh correlation
Explained by: Social support is highly correlated with Explained by: Healthy life expectancy and 12 other fieldsHigh correlation
Perceptions of corruption is highly correlated with Ladder score and 6 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Explained by: Healthy life expectancy and 13 other fieldsHigh correlation
Logged GDP per capita is highly correlated with Explained by: Healthy life expectancy and 13 other fieldsHigh correlation
Explained by: Perceptions of corruption is highly correlated with Ladder score and 6 other fieldsHigh correlation
lowerwhisker is highly correlated with Explained by: Healthy life expectancy and 13 other fieldsHigh correlation
Standard error of ladder score is highly correlated with Explained by: Healthy life expectancy and 5 other fieldsHigh correlation
Explained by: Generosity is highly correlated with Explained by: Healthy life expectancy and 3 other fieldsHigh correlation
Ladder score in Dystopia is highly correlated with Regional indicatorHigh correlation
Regional indicator is highly correlated with Ladder score in DystopiaHigh correlation
Country name is uniformly distributed Uniform
Country name has unique values Unique

Reproduction

Analysis started2021-07-11 17:11:21.036827
Analysis finished2021-07-11 17:11:38.260308
Duration17.22 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Country name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Finland
 
1
Ghana
 
1
Turkmenistan
 
1
Gambia
 
1
Benin
 
1
Other values (144)
144 

Length

Max length25
Median length7
Mean length8.268456376
Min length4

Characters and Unicode

Total characters1232
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)100.0%

Sample

1st rowFinland
2nd rowDenmark
3rd rowSwitzerland
4th rowIceland
5th rowNetherlands

Common Values

ValueCountFrequency (%)
Finland1
 
0.7%
Ghana1
 
0.7%
Turkmenistan1
 
0.7%
Gambia1
 
0.7%
Benin1
 
0.7%
Laos1
 
0.7%
Bangladesh1
 
0.7%
Guinea1
 
0.7%
South Africa1
 
0.7%
Turkey1
 
0.7%
Other values (139)139
93.3%

Length

2021-07-11T13:11:38.400581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
china3
 
1.7%
united3
 
1.7%
cyprus2
 
1.1%
north2
 
1.1%
republic2
 
1.1%
of2
 
1.1%
south2
 
1.1%
bangladesh1
 
0.6%
niger1
 
0.6%
turkmenistan1
 
0.6%
Other values (159)159
89.3%

Most occurring characters

ValueCountFrequency (%)
a191
15.5%
i110
 
8.9%
n96
 
7.8%
e81
 
6.6%
r72
 
5.8%
o71
 
5.8%
l44
 
3.6%
t44
 
3.6%
s39
 
3.2%
u39
 
3.2%
Other values (42)445
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1021
82.9%
Uppercase Letter177
 
14.4%
Space Separator29
 
2.4%
Other Punctuation3
 
0.2%
Open Punctuation1
 
0.1%
Close Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a191
18.7%
i110
10.8%
n96
9.4%
e81
 
7.9%
r72
 
7.1%
o71
 
7.0%
l44
 
4.3%
t44
 
4.3%
s39
 
3.8%
u39
 
3.8%
Other values (16)234
22.9%
Uppercase Letter
ValueCountFrequency (%)
S17
 
9.6%
C17
 
9.6%
M16
 
9.0%
B13
 
7.3%
A12
 
6.8%
N10
 
5.6%
L10
 
5.6%
I9
 
5.1%
T9
 
5.1%
P9
 
5.1%
Other values (12)55
31.1%
Space Separator
ValueCountFrequency (%)
29
100.0%
Other Punctuation
ValueCountFrequency (%)
.3
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1198
97.2%
Common34
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a191
15.9%
i110
 
9.2%
n96
 
8.0%
e81
 
6.8%
r72
 
6.0%
o71
 
5.9%
l44
 
3.7%
t44
 
3.7%
s39
 
3.3%
u39
 
3.3%
Other values (38)411
34.3%
Common
ValueCountFrequency (%)
29
85.3%
.3
 
8.8%
(1
 
2.9%
)1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a191
15.5%
i110
 
8.9%
n96
 
7.8%
e81
 
6.6%
r72
 
5.8%
o71
 
5.8%
l44
 
3.6%
t44
 
3.6%
s39
 
3.2%
u39
 
3.2%
Other values (42)445
36.1%

Regional indicator
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Sub-Saharan Africa
36 
Western Europe
21 
Latin America and Caribbean
20 
Middle East and North Africa
17 
Central and Eastern Europe
17 
Other values (5)
38 

Length

Max length34
Median length18
Mean length21.08724832
Min length9

Characters and Unicode

Total characters3142
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWestern Europe
2nd rowWestern Europe
3rd rowWestern Europe
4th rowWestern Europe
5th rowWestern Europe

Common Values

ValueCountFrequency (%)
Sub-Saharan Africa36
24.2%
Western Europe21
14.1%
Latin America and Caribbean20
13.4%
Middle East and North Africa17
11.4%
Central and Eastern Europe17
11.4%
Commonwealth of Independent States12
 
8.1%
Southeast Asia9
 
6.0%
South Asia7
 
4.7%
East Asia6
 
4.0%
North America and ANZ4
 
2.7%

Length

2021-07-11T13:11:38.524103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-11T13:11:38.570927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
and58
12.7%
africa53
 
11.6%
europe38
 
8.4%
sub-saharan36
 
7.9%
america24
 
5.3%
east23
 
5.1%
asia22
 
4.8%
western21
 
4.6%
north21
 
4.6%
caribbean20
 
4.4%
Other values (11)139
30.5%

Most occurring characters

ValueCountFrequency (%)
a415
13.2%
306
 
9.7%
r247
 
7.9%
e244
 
7.8%
n237
 
7.5%
t192
 
6.1%
i156
 
5.0%
d116
 
3.7%
o111
 
3.5%
s104
 
3.3%
Other values (20)1014
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2371
75.5%
Uppercase Letter429
 
13.7%
Space Separator306
 
9.7%
Dash Punctuation36
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a415
17.5%
r247
10.4%
e244
10.3%
n237
10.0%
t192
8.1%
i156
 
6.6%
d116
 
4.9%
o111
 
4.7%
s104
 
4.4%
u90
 
3.8%
Other values (8)459
19.4%
Uppercase Letter
ValueCountFrequency (%)
A103
24.0%
S100
23.3%
E78
18.2%
C49
11.4%
N25
 
5.8%
W21
 
4.9%
L20
 
4.7%
M17
 
4.0%
I12
 
2.8%
Z4
 
0.9%
Space Separator
ValueCountFrequency (%)
306
100.0%
Dash Punctuation
ValueCountFrequency (%)
-36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2800
89.1%
Common342
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a415
14.8%
r247
 
8.8%
e244
 
8.7%
n237
 
8.5%
t192
 
6.9%
i156
 
5.6%
d116
 
4.1%
o111
 
4.0%
s104
 
3.7%
A103
 
3.7%
Other values (18)875
31.2%
Common
ValueCountFrequency (%)
306
89.5%
-36
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a415
13.2%
306
 
9.7%
r247
 
7.9%
e244
 
7.8%
n237
 
7.5%
t192
 
6.1%
i156
 
5.0%
d116
 
3.7%
o111
 
3.5%
s104
 
3.3%
Other values (20)1014
32.3%

Ladder score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct147
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.532838926
Minimum2.523
Maximum7.842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:38.657468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.523
5-th percentile3.637
Q14.852
median5.534
Q36.255
95-th percentile7.3052
Maximum7.842
Range5.319
Interquartile range (IQR)1.403

Descriptive statistics

Standard deviation1.073923566
Coefficient of variation (CV)0.1940999151
Kurtosis-0.3685016279
Mean5.532838926
Median Absolute Deviation (MAD)0.7
Skewness-0.10426854
Sum824.393
Variance1.153311825
MonotonicityDecreasing
2021-07-11T13:11:38.736908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.9292
 
1.3%
5.4772
 
1.3%
4.8541
 
0.7%
4.9841
 
0.7%
5.0741
 
0.7%
5.0661
 
0.7%
5.0511
 
0.7%
5.0451
 
0.7%
5.031
 
0.7%
5.0251
 
0.7%
Other values (137)137
91.9%
ValueCountFrequency (%)
2.5231
0.7%
3.1451
0.7%
3.4151
0.7%
3.4671
0.7%
3.5121
0.7%
3.61
0.7%
3.6151
0.7%
3.6231
0.7%
3.6581
0.7%
3.7751
0.7%
ValueCountFrequency (%)
7.8421
0.7%
7.621
0.7%
7.5711
0.7%
7.5541
0.7%
7.4641
0.7%
7.3921
0.7%
7.3631
0.7%
7.3241
0.7%
7.2771
0.7%
7.2681
0.7%

Standard error of ladder score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct65
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05875167785
Minimum0.026
Maximum0.173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:38.811703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.026
5-th percentile0.0344
Q10.043
median0.054
Q30.07
95-th percentile0.0958
Maximum0.173
Range0.147
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.02200119961
Coefficient of variation (CV)0.3744778092
Kurtosis6.329840165
Mean0.05875167785
Median Absolute Deviation (MAD)0.013
Skewness1.891583644
Sum8.754
Variance0.0004840527843
MonotonicityNot monotonic
2021-07-11T13:11:38.886857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0467
 
4.7%
0.046
 
4.0%
0.0495
 
3.4%
0.0365
 
3.4%
0.0595
 
3.4%
0.0685
 
3.4%
0.0454
 
2.7%
0.0474
 
2.7%
0.0534
 
2.7%
0.0424
 
2.7%
Other values (55)100
67.1%
ValueCountFrequency (%)
0.0261
 
0.7%
0.0271
 
0.7%
0.0291
 
0.7%
0.0321
 
0.7%
0.0331
 
0.7%
0.0343
2.0%
0.0352
 
1.3%
0.0365
3.4%
0.0372
 
1.3%
0.0384
2.7%
ValueCountFrequency (%)
0.1731
0.7%
0.1561
0.7%
0.121
0.7%
0.1071
0.7%
0.1061
0.7%
0.1031
0.7%
0.1021
0.7%
0.0971
0.7%
0.0941
0.7%
0.0922
1.3%

upperwhisker
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct146
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.648006711
Minimum2.596
Maximum7.904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:38.963662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.596
5-th percentile3.824
Q14.991
median5.625
Q36.344
95-th percentile7.3796
Maximum7.904
Range5.308
Interquartile range (IQR)1.353

Descriptive statistics

Standard deviation1.054329622
Coefficient of variation (CV)0.18667287
Kurtosis-0.3329319299
Mean5.648006711
Median Absolute Deviation (MAD)0.687
Skewness-0.1161623381
Sum841.553
Variance1.111610953
MonotonicityNot monotonic
2021-07-11T13:11:39.039415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4542
 
1.3%
5.3972
 
1.3%
4.9982
 
1.3%
5.2191
 
0.7%
5.2731
 
0.7%
5.1361
 
0.7%
5.2251
 
0.7%
5.1891
 
0.7%
5.1191
 
0.7%
5.1151
 
0.7%
Other values (136)136
91.3%
ValueCountFrequency (%)
2.5961
0.7%
3.2591
0.7%
3.5481
0.7%
3.6111
0.7%
3.7481
0.7%
3.7621
0.7%
3.7811
0.7%
3.7941
0.7%
3.8691
0.7%
3.9531
0.7%
ValueCountFrequency (%)
7.9041
0.7%
7.6871
0.7%
7.671
0.7%
7.6431
0.7%
7.5181
0.7%
7.4621
0.7%
7.4331
0.7%
7.3961
0.7%
7.3551
0.7%
7.3371
0.7%

lowerwhisker
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.417630872
Minimum2.449
Maximum7.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:39.111255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.449
5-th percentile3.4994
Q14.706
median5.413
Q36.128
95-th percentile7.2304
Maximum7.78
Range5.331
Interquartile range (IQR)1.422

Descriptive statistics

Standard deviation1.094879053
Coefficient of variation (CV)0.2020955429
Kurtosis-0.3912602512
Mean5.417630872
Median Absolute Deviation (MAD)0.715
Skewness-0.09659054314
Sum807.227
Variance1.19876014
MonotonicityNot monotonic
2021-07-11T13:11:39.189273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.6532
 
1.3%
5.0012
 
1.3%
4.9962
 
1.3%
3.2762
 
1.3%
7.1982
 
1.3%
5.9332
 
1.3%
7.781
 
0.7%
4.9011
 
0.7%
4.9411
 
0.7%
4.9341
 
0.7%
Other values (133)133
89.3%
ValueCountFrequency (%)
2.4491
0.7%
3.031
0.7%
3.2762
1.3%
3.2821
0.7%
3.3221
0.7%
3.4191
0.7%
3.4851
0.7%
3.5211
0.7%
3.5651
0.7%
3.6981
0.7%
ValueCountFrequency (%)
7.781
0.7%
7.5521
0.7%
7.51
0.7%
7.4381
0.7%
7.411
0.7%
7.3231
0.7%
7.2931
0.7%
7.2521
0.7%
7.1982
1.3%
7.1021
0.7%

Logged GDP per capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct148
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.432208054
Minimum6.635
Maximum11.647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:39.260557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6.635
5-th percentile7.4112
Q18.541
median9.569
Q310.421
95-th percentile10.9732
Maximum11.647
Range5.012
Interquartile range (IQR)1.88

Descriptive statistics

Standard deviation1.158601448
Coefficient of variation (CV)0.1228345941
Kurtosis-0.8154072873
Mean9.432208054
Median Absolute Deviation (MAD)0.987
Skewness-0.3520251121
Sum1405.399
Variance1.342357315
MonotonicityNot monotonic
2021-07-11T13:11:39.332965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.5762
 
1.3%
7.0981
 
0.7%
7.6861
 
0.7%
8.0871
 
0.7%
8.9471
 
0.7%
8.4541
 
0.7%
7.8381
 
0.7%
9.4031
 
0.7%
10.241
 
0.7%
8.4581
 
0.7%
Other values (138)138
92.6%
ValueCountFrequency (%)
6.6351
0.7%
6.9581
0.7%
7.0981
0.7%
7.1581
0.7%
7.2881
0.7%
7.3621
0.7%
7.3641
0.7%
7.3961
0.7%
7.4341
0.7%
7.4771
0.7%
ValueCountFrequency (%)
11.6471
0.7%
11.4881
0.7%
11.3421
0.7%
11.1171
0.7%
11.0851
0.7%
11.0531
0.7%
11.0231
0.7%
111
0.7%
10.9331
0.7%
10.9321
0.7%

Social support
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct119
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8147449664
Minimum0.463
Maximum0.983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:39.404181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.463
5-th percentile0.5826
Q10.75
median0.832
Q30.905
95-th percentile0.9476
Maximum0.983
Range0.52
Interquartile range (IQR)0.155

Descriptive statistics

Standard deviation0.1148890272
Coefficient of variation (CV)0.1410122577
Kurtosis0.3949403248
Mean0.8147449664
Median Absolute Deviation (MAD)0.076
Skewness-0.9378102171
Sum121.397
Variance0.01319948857
MonotonicityNot monotonic
2021-07-11T13:11:39.482042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9543
 
2.0%
0.713
 
2.0%
0.8323
 
2.0%
0.9423
 
2.0%
0.9343
 
2.0%
0.8983
 
2.0%
0.8112
 
1.3%
0.8272
 
1.3%
0.862
 
1.3%
0.8212
 
1.3%
Other values (109)123
82.6%
ValueCountFrequency (%)
0.4631
0.7%
0.4891
0.7%
0.491
0.7%
0.5371
0.7%
0.541
0.7%
0.5521
0.7%
0.561
0.7%
0.5691
0.7%
0.6031
0.7%
0.6191
0.7%
ValueCountFrequency (%)
0.9832
1.3%
0.9543
2.0%
0.9521
 
0.7%
0.9482
1.3%
0.9472
1.3%
0.9431
 
0.7%
0.9423
2.0%
0.9411
 
0.7%
0.941
 
0.7%
0.9391
 
0.7%

Healthy life expectancy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct135
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.99279866
Minimum48.478
Maximum76.953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:39.555063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum48.478
5-th percentile52.8558
Q159.802
median66.603
Q369.6
95-th percentile73.8992
Maximum76.953
Range28.475
Interquartile range (IQR)9.798

Descriptive statistics

Standard deviation6.76204309
Coefficient of variation (CV)0.1040429591
Kurtosis-0.5641938609
Mean64.99279866
Median Absolute Deviation (MAD)4.605
Skewness-0.5219979782
Sum9683.927
Variance45.72522676
MonotonicityNot monotonic
2021-07-11T13:11:39.623546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.63
 
2.0%
71.42
 
1.3%
622
 
1.3%
72.72
 
1.3%
73.82
 
1.3%
72.52
 
1.3%
68.82
 
1.3%
73.92
 
1.3%
73.32
 
1.3%
72.42
 
1.3%
Other values (125)128
85.9%
ValueCountFrequency (%)
48.4781
0.7%
48.71
0.7%
50.1021
0.7%
50.1141
0.7%
50.8331
0.7%
51.6511
0.7%
51.9691
0.7%
52.4931
0.7%
53.41
0.7%
53.5151
0.7%
ValueCountFrequency (%)
76.9531
0.7%
76.821
0.7%
75.11
0.7%
74.71
0.7%
74.41
0.7%
741
0.7%
73.92
1.3%
73.8982
1.3%
73.82
1.3%
73.5031
0.7%

Freedom to make life choices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct126
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7915973154
Minimum0.382
Maximum0.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:39.695712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.382
5-th percentile0.5802
Q10.718
median0.804
Q30.877
95-th percentile0.943
Maximum0.97
Range0.588
Interquartile range (IQR)0.159

Descriptive statistics

Standard deviation0.1133317851
Coefficient of variation (CV)0.1431684808
Kurtosis0.4083134091
Mean0.7915973154
Median Absolute Deviation (MAD)0.08
Skewness-0.7547987468
Sum117.948
Variance0.01284409351
MonotonicityNot monotonic
2021-07-11T13:11:39.768899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8223
 
2.0%
0.6973
 
2.0%
0.8773
 
2.0%
0.6953
 
2.0%
0.9492
 
1.3%
0.7152
 
1.3%
0.9272
 
1.3%
0.8412
 
1.3%
0.8672
 
1.3%
0.7082
 
1.3%
Other values (116)125
83.9%
ValueCountFrequency (%)
0.3821
0.7%
0.481
0.7%
0.5251
0.7%
0.5481
0.7%
0.5521
0.7%
0.5611
0.7%
0.5761
0.7%
0.5791
0.7%
0.5821
0.7%
0.5931
0.7%
ValueCountFrequency (%)
0.971
0.7%
0.961
0.7%
0.9591
0.7%
0.9551
0.7%
0.9492
1.3%
0.9461
0.7%
0.9451
0.7%
0.941
0.7%
0.9351
0.7%
0.9341
0.7%

Generosity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct130
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01513422819
Minimum-0.288
Maximum0.542
Zeros0
Zeros (%)0.0%
Negative86
Negative (%)57.7%
Memory size1.3 KiB
2021-07-11T13:11:39.845559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.288
5-th percentile-0.2146
Q1-0.126
median-0.036
Q30.079
95-th percentile0.2666
Maximum0.542
Range0.83
Interquartile range (IQR)0.205

Descriptive statistics

Standard deviation0.1506567002
Coefficient of variation (CV)-9.954699926
Kurtosis1.636378871
Mean-0.01513422819
Median Absolute Deviation (MAD)0.101
Skewness1.009955989
Sum-2.255
Variance0.02269744132
MonotonicityNot monotonic
2021-07-11T13:11:39.914952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0893
 
2.0%
0.0613
 
2.0%
-0.0343
 
2.0%
-0.1473
 
2.0%
-0.0982
 
1.3%
-0.1062
 
1.3%
-0.0672
 
1.3%
-0.1242
 
1.3%
0.1232
 
1.3%
0.0382
 
1.3%
Other values (120)125
83.9%
ValueCountFrequency (%)
-0.2881
0.7%
-0.2581
0.7%
-0.2461
0.7%
-0.2441
0.7%
-0.2381
0.7%
-0.2361
0.7%
-0.2231
0.7%
-0.2191
0.7%
-0.2081
0.7%
-0.2031
0.7%
ValueCountFrequency (%)
0.5421
0.7%
0.5091
0.7%
0.4241
0.7%
0.4221
0.7%
0.3111
0.7%
0.2872
1.3%
0.2731
0.7%
0.2571
0.7%
0.2331
0.7%
0.2181
0.7%

Perceptions of corruption
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct130
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7274496644
Minimum0.082
Maximum0.939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:39.986234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.082
5-th percentile0.3104
Q10.667
median0.781
Q30.845
95-th percentile0.9162
Maximum0.939
Range0.857
Interquartile range (IQR)0.178

Descriptive statistics

Standard deviation0.1792263191
Coefficient of variation (CV)0.2463762482
Kurtosis2.249550499
Mean0.7274496644
Median Absolute Deviation (MAD)0.079
Skewness-1.577464564
Sum108.39
Variance0.03212207346
MonotonicityNot monotonic
2021-07-11T13:11:40.056483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8013
 
2.0%
0.7142
 
1.3%
0.8092
 
1.3%
0.8272
 
1.3%
0.9242
 
1.3%
0.8662
 
1.3%
0.7452
 
1.3%
0.6842
 
1.3%
0.82
 
1.3%
0.7212
 
1.3%
Other values (120)128
85.9%
ValueCountFrequency (%)
0.0821
0.7%
0.1671
0.7%
0.1791
0.7%
0.1861
0.7%
0.2371
0.7%
0.2421
0.7%
0.271
0.7%
0.2921
0.7%
0.3381
0.7%
0.3631
0.7%
ValueCountFrequency (%)
0.9391
0.7%
0.9381
0.7%
0.9321
0.7%
0.9311
0.7%
0.9242
1.3%
0.9181
0.7%
0.9171
0.7%
0.9151
0.7%
0.9111
0.7%
0.9081
0.7%

Ladder score in Dystopia
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2.43
149 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters596
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.43
2nd row2.43
3rd row2.43
4th row2.43
5th row2.43

Common Values

ValueCountFrequency (%)
2.43149
100.0%

Length

2021-07-11T13:11:40.172170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-11T13:11:40.207579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.43149
100.0%

Most occurring characters

ValueCountFrequency (%)
2149
25.0%
.149
25.0%
4149
25.0%
3149
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number447
75.0%
Other Punctuation149
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2149
33.3%
4149
33.3%
3149
33.3%
Other Punctuation
ValueCountFrequency (%)
.149
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common596
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2149
25.0%
.149
25.0%
4149
25.0%
3149
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2149
25.0%
.149
25.0%
4149
25.0%
3149
25.0%

Explained by: Log GDP per capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct138
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9771610738
Minimum0
Maximum1.751
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:40.253081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2712
Q10.666
median1.025
Q31.323
95-th percentile1.5158
Maximum1.751
Range1.751
Interquartile range (IQR)0.657

Descriptive statistics

Standard deviation0.404739941
Coefficient of variation (CV)0.4141998201
Kurtosis-0.8155980203
Mean0.9771610738
Median Absolute Deviation (MAD)0.345
Skewness-0.3520349646
Sum145.597
Variance0.1638144198
MonotonicityNot monotonic
2021-07-11T13:11:40.326365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3643
 
2.0%
1.032
 
1.3%
0.5182
 
1.3%
0.372
 
1.3%
0.8452
 
1.3%
1.482
 
1.3%
0.6032
 
1.3%
0.6662
 
1.3%
0.9542
 
1.3%
1.3772
 
1.3%
Other values (128)128
85.9%
ValueCountFrequency (%)
01
0.7%
0.1131
0.7%
0.1621
0.7%
0.1831
0.7%
0.2281
0.7%
0.2541
0.7%
0.2551
0.7%
0.2661
0.7%
0.2791
0.7%
0.2941
0.7%
ValueCountFrequency (%)
1.7511
0.7%
1.6951
0.7%
1.6441
0.7%
1.5661
0.7%
1.5551
0.7%
1.5431
0.7%
1.5331
0.7%
1.5251
0.7%
1.5021
0.7%
1.5011
0.7%

Explained by: Social support
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct135
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7933154362
Minimum0
Maximum1.172
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:40.402212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2698
Q10.647
median0.832
Q30.996
95-th percentile1.0926
Maximum1.172
Range1.172
Interquartile range (IQR)0.349

Descriptive statistics

Standard deviation0.2588712528
Coefficient of variation (CV)0.3263156633
Kurtosis0.3997424145
Mean0.7933154362
Median Absolute Deviation (MAD)0.171
Skewness-0.9387417329
Sum118.204
Variance0.0670143255
MonotonicityNot monotonic
2021-07-11T13:11:40.478196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0793
 
2.0%
1.0623
 
2.0%
0.7652
 
1.3%
1.1722
 
1.3%
0.7582
 
1.3%
0.8952
 
1.3%
0.8412
 
1.3%
1.1082
 
1.3%
0.9822
 
1.3%
1.0652
 
1.3%
Other values (125)127
85.2%
ValueCountFrequency (%)
01
0.7%
0.0581
0.7%
0.0621
0.7%
0.1681
0.7%
0.1731
0.7%
0.2021
0.7%
0.2191
0.7%
0.2391
0.7%
0.3161
0.7%
0.3531
0.7%
ValueCountFrequency (%)
1.1722
1.3%
1.1082
1.3%
1.1061
0.7%
1.1031
0.7%
1.0941
0.7%
1.0931
0.7%
1.0921
0.7%
1.091
0.7%
1.0831
0.7%
1.0811
0.7%

Explained by: Healthy life expectancy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct119
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5201610738
Minimum0
Maximum0.897
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:40.553818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1376
Q10.357
median0.571
Q30.665
95-th percentile0.801
Maximum0.897
Range0.897
Interquartile range (IQR)0.308

Descriptive statistics

Standard deviation0.2130190978
Coefficient of variation (CV)0.4095252578
Kurtosis-0.5648848141
Mean0.5201610738
Median Absolute Deviation (MAD)0.145
Skewness-0.5215856573
Sum77.504
Variance0.04537713604
MonotonicityNot monotonic
2021-07-11T13:11:40.628638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8014
 
2.7%
0.4263
 
2.0%
0.763
 
2.0%
0.642
 
1.3%
0.5712
 
1.3%
0.6462
 
1.3%
0.7632
 
1.3%
0.592
 
1.3%
0.6342
 
1.3%
0.4982
 
1.3%
Other values (109)125
83.9%
ValueCountFrequency (%)
01
0.7%
0.0071
0.7%
0.0511
0.7%
0.0521
0.7%
0.0741
0.7%
0.11
0.7%
0.111
0.7%
0.1261
0.7%
0.1551
0.7%
0.1591
0.7%
ValueCountFrequency (%)
0.8971
 
0.7%
0.8931
 
0.7%
0.8381
 
0.7%
0.8261
 
0.7%
0.8161
 
0.7%
0.8041
 
0.7%
0.8014
2.7%
0.7982
1.3%
0.7881
 
0.7%
0.7851
 
0.7%

Explained by: Freedom to make life choices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct130
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4987114094
Minimum0
Maximum0.716
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:40.702257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2412
Q10.409
median0.514
Q30.603
95-th percentile0.6826
Maximum0.716
Range0.716
Interquartile range (IQR)0.194

Descriptive statistics

Standard deviation0.1378883849
Coefficient of variation (CV)0.2764893329
Kurtosis0.414352537
Mean0.4987114094
Median Absolute Deviation (MAD)0.097
Skewness-0.7570961383
Sum74.308
Variance0.01901320669
MonotonicityNot monotonic
2021-07-11T13:11:40.777084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3813
 
2.0%
0.3843
 
2.0%
0.5363
 
2.0%
0.62
 
1.3%
0.4052
 
1.3%
0.6032
 
1.3%
0.5542
 
1.3%
0.582
 
1.3%
0.6732
 
1.3%
0.3972
 
1.3%
Other values (120)126
84.6%
ValueCountFrequency (%)
01
0.7%
0.1191
0.7%
0.1751
0.7%
0.2021
0.7%
0.2071
0.7%
0.2181
0.7%
0.2361
0.7%
0.241
0.7%
0.2431
0.7%
0.2571
0.7%
ValueCountFrequency (%)
0.7161
0.7%
0.7031
0.7%
0.7021
0.7%
0.6981
0.7%
0.6911
0.7%
0.691
0.7%
0.6861
0.7%
0.6851
0.7%
0.6791
0.7%
0.6732
1.3%

Explained by: Generosity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct119
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1780469799
Minimum0
Maximum0.541
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:40.853864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0478
Q10.105
median0.164
Q30.239
95-th percentile0.362
Maximum0.541
Range0.541
Interquartile range (IQR)0.134

Descriptive statistics

Standard deviation0.09827033423
Coefficient of variation (CV)0.5519348562
Kurtosis1.633868464
Mean0.1780469799
Median Absolute Deviation (MAD)0.065
Skewness1.009699408
Sum26.529
Variance0.009657058589
MonotonicityNot monotonic
2021-07-11T13:11:40.931042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0793
 
2.0%
0.2413
 
2.0%
0.1663
 
2.0%
0.1443
 
2.0%
0.0923
 
2.0%
0.2463
 
2.0%
0.1242
 
1.3%
0.2682
 
1.3%
0.0692
 
1.3%
0.1222
 
1.3%
Other values (109)123
82.6%
ValueCountFrequency (%)
01
0.7%
0.021
0.7%
0.0271
0.7%
0.0291
0.7%
0.0321
0.7%
0.0341
0.7%
0.0431
0.7%
0.0451
0.7%
0.0521
0.7%
0.0561
0.7%
ValueCountFrequency (%)
0.5411
0.7%
0.521
0.7%
0.4651
0.7%
0.4631
0.7%
0.3911
0.7%
0.3752
1.3%
0.3661
0.7%
0.3561
0.7%
0.341
0.7%
0.331
0.7%

Explained by: Perceptions of corruption
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct117
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1351409396
Minimum0
Maximum0.547
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:41.003204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0144
Q10.06
median0.101
Q30.174
95-th percentile0.4014
Maximum0.547
Range0.547
Interquartile range (IQR)0.114

Descriptive statistics

Standard deviation0.114361389
Coefficient of variation (CV)0.8462379303
Kurtosis2.252409198
Mean0.1351409396
Median Absolute Deviation (MAD)0.05
Skewness1.577898143
Sum20.136
Variance0.0130785273
MonotonicityNot monotonic
2021-07-11T13:11:41.074416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0733
 
2.0%
0.0643
 
2.0%
0.0893
 
2.0%
0.0883
 
2.0%
0.1393
 
2.0%
0.0922
 
1.3%
0.0752
 
1.3%
0.012
 
1.3%
0.0532
 
1.3%
0.0612
 
1.3%
Other values (107)124
83.2%
ValueCountFrequency (%)
01
0.7%
0.0011
0.7%
0.0052
1.3%
0.012
1.3%
0.0131
0.7%
0.0141
0.7%
0.0151
0.7%
0.0181
0.7%
0.021
0.7%
0.0221
0.7%
ValueCountFrequency (%)
0.5471
0.7%
0.4931
0.7%
0.4851
0.7%
0.4811
0.7%
0.4481
0.7%
0.4451
0.7%
0.4271
0.7%
0.4131
0.7%
0.3841
0.7%
0.3671
0.7%

Dystopia + residual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct142
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.430328859
Minimum0.648
Maximum3.482
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2021-07-11T13:11:41.147279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.648
5-th percentile1.4058
Q12.138
median2.509
Q32.794
95-th percentile3.2132
Maximum3.482
Range2.834
Interquartile range (IQR)0.656

Descriptive statistics

Standard deviation0.5376452091
Coefficient of variation (CV)0.2212232337
Kurtosis0.4478822739
Mean2.430328859
Median Absolute Deviation (MAD)0.321
Skewness-0.5587955595
Sum362.119
Variance0.2890623709
MonotonicityNot monotonic
2021-07-11T13:11:41.217796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8232
 
1.3%
2.1482
 
1.3%
2.6822
 
1.3%
2.6532
 
1.3%
2.7942
 
1.3%
2.7842
 
1.3%
2.5962
 
1.3%
3.2531
 
0.7%
2.991
 
0.7%
3.4821
 
0.7%
Other values (132)132
88.6%
ValueCountFrequency (%)
0.6481
0.7%
1.0751
0.7%
1.0951
0.7%
1.2051
0.7%
1.2361
0.7%
1.2631
0.7%
1.3791
0.7%
1.4051
0.7%
1.4071
0.7%
1.4091
0.7%
ValueCountFrequency (%)
3.4821
0.7%
3.4761
0.7%
3.471
0.7%
3.4691
0.7%
3.3871
0.7%
3.3751
0.7%
3.2531
0.7%
3.2161
0.7%
3.2091
0.7%
3.1951
0.7%

Interactions

2021-07-11T13:11:21.420563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:21.475483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:21.534623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:21.587861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:21.639482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:21.690265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:21.741839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:21.794065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:21.847724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:21.897187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:21.948537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.006210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.061981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.114753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.172070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.225690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.280539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.333668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.393794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.458609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.518471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.577461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.637075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.698571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.760412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.823160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:22.882248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.100811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.165951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.228870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.289816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.355569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.417075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.480145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.542806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.594697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.653806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.704235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.755611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.806788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.857349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.908279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:23.960239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.007889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.056593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.112609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.165421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.216313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.271391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.322595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.374976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.426563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.476110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.533747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.584598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.634633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.684065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.737864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.789439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.841571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:24.968724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.018182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.073875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.128156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.181164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.237374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.291883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.348448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.401122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.451102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.508282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.559043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.608346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.658713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.710244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.762287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.814243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.861445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.910136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:25.964659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.018272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.068850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.124960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.176615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.228443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.279386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.331416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.390987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.442607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.494093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.545934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.598755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.651222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.708716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.758551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.808995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.865495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.919908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:26.972859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.030567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.085433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.238495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.292273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.343504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.402589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.454951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.506791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.558643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.612772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.667733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.724894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.777724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.830615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.890463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:27.947963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.001578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.057848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.111550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.165830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.219057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.272217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.331420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.383673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.436332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.488869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.542274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.597174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.651783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.702452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.753272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.812119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.868224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.922179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:28.980367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.034866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.089844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.143836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.190754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.243728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.290793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.337857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.384614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.432375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.480650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.529134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.574287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.620833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.673248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.723360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.771901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.824510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:29.872922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.040693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.094147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.143097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.198737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.247515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.296315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.344967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.395350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.446187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.496475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.543341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.590694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.644432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.696687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.746850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.801357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.850974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.902160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:30.952270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.009564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.073937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.143483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.207158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.264664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.322719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.380246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.438788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.492783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.549414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.611414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.670834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.728132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.789895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.847867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.906845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:31.964351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.018820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.078808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.132969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.187078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.240562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.295047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.350016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.406120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.458040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.510735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.569402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.625853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.681139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.740783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.796657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.853023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.908267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:32.958403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.014842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.064999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.115379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.165490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.221850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.273219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.325406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.374409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.567481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.623489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.676633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.728606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.784717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.837195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.890541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.942054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:33.999496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.063120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.123399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.180480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.237511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.295875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.353498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.412614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.467530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.523851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.585858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.646110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.703884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.765486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.823623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.883117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.941164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:34.992758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.051787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.104278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.156698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.208151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.261987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.320376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.374583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.425096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.476071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.532381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.586772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.639675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.696816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.749630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.804084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.857138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.909591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:35.968784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.021835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.075149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.127531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.181622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.235295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.289943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.340744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.392242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.449945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.508294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.562416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.620528image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.674812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.730505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.784490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.836107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.894468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.946255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:36.998025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.049344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.102201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.154884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.208388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.258027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.308319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.365394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.420872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.473413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.530448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.583574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-11T13:11:37.638047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-11T13:11:41.297977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-11T13:11:41.472322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-11T13:11:41.637375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-11T13:11:41.800405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-11T13:11:41.940600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-11T13:11:37.938274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-11T13:11:38.174640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Country nameRegional indicatorLadder scoreStandard error of ladder scoreupperwhiskerlowerwhiskerLogged GDP per capitaSocial supportHealthy life expectancyFreedom to make life choicesGenerosityPerceptions of corruptionLadder score in DystopiaExplained by: Log GDP per capitaExplained by: Social supportExplained by: Healthy life expectancyExplained by: Freedom to make life choicesExplained by: GenerosityExplained by: Perceptions of corruptionDystopia + residual
0FinlandWestern Europe7.8420.0327.9047.78010.7750.95472.00.949-0.0980.1862.431.4461.1060.7410.6910.1240.4813.253
1DenmarkWestern Europe7.6200.0357.6877.55210.9330.95472.70.9460.0300.1792.431.5021.1080.7630.6860.2080.4852.868
2SwitzerlandWestern Europe7.5710.0367.6437.50011.1170.94274.40.9190.0250.2922.431.5661.0790.8160.6530.2040.4132.839
3IcelandWestern Europe7.5540.0597.6707.43810.8780.98373.00.9550.1600.6732.431.4821.1720.7720.6980.2930.1702.967
4NetherlandsWestern Europe7.4640.0277.5187.41010.9320.94272.40.9130.1750.3382.431.5011.0790.7530.6470.3020.3842.798
5NorwayWestern Europe7.3920.0357.4627.32311.0530.95473.30.9600.0930.2702.431.5431.1080.7820.7030.2490.4272.580
6SwedenWestern Europe7.3630.0367.4337.29310.8670.93472.70.9450.0860.2372.431.4781.0620.7630.6850.2440.4482.683
7LuxembourgWestern Europe7.3240.0377.3967.25211.6470.90872.60.907-0.0340.3862.431.7511.0030.7600.6390.1660.3532.653
8New ZealandNorth America and ANZ7.2770.0407.3557.19810.6430.94873.40.9290.1340.2422.431.4001.0940.7850.6650.2760.4452.612
9AustriaWestern Europe7.2680.0367.3377.19810.9060.93473.30.9080.0420.4812.431.4921.0620.7820.6400.2150.2922.784

Last rows

Country nameRegional indicatorLadder scoreStandard error of ladder scoreupperwhiskerlowerwhiskerLogged GDP per capitaSocial supportHealthy life expectancyFreedom to make life choicesGenerosityPerceptions of corruptionLadder score in DystopiaExplained by: Log GDP per capitaExplained by: Social supportExplained by: Healthy life expectancyExplained by: Freedom to make life choicesExplained by: GenerosityExplained by: Perceptions of corruptionDystopia + residual
139BurundiSub-Saharan Africa3.7750.1073.9853.5656.6350.49053.4000.626-0.0240.6072.430.0000.0620.1550.2980.1720.2122.876
140YemenMiddle East and North Africa3.6580.0703.7943.5217.5780.83257.1220.602-0.1470.8002.430.3290.8310.2720.2680.0920.0891.776
141TanzaniaSub-Saharan Africa3.6230.0713.7623.4857.8760.70257.9990.8330.1830.5772.430.4330.5400.3000.5490.3070.2311.263
142HaitiLatin America and Caribbean3.6150.1733.9533.2767.4770.54055.7000.5930.4220.7212.430.2940.1730.2270.2570.4630.1392.060
143MalawiSub-Saharan Africa3.6000.0923.7813.4196.9580.53757.9480.7800.0380.7292.430.1130.1680.2980.4840.2130.1342.190
144LesothoSub-Saharan Africa3.5120.1203.7483.2767.9260.78748.7000.715-0.1310.9152.430.4510.7310.0070.4050.1030.0151.800
145BotswanaSub-Saharan Africa3.4670.0743.6113.3229.7820.78459.2690.824-0.2460.8012.431.0990.7240.3400.5390.0270.0880.648
146RwandaSub-Saharan Africa3.4150.0683.5483.2827.6760.55261.4000.8970.0610.1672.430.3640.2020.4070.6270.2270.4931.095
147ZimbabweSub-Saharan Africa3.1450.0583.2593.0307.9430.75056.2010.677-0.0470.8212.430.4570.6490.2430.3590.1570.0751.205
148AfghanistanSouth Asia2.5230.0382.5962.4497.6950.46352.4930.382-0.1020.9242.430.3700.0000.1260.0000.1220.0101.895